Dynamically scaling spatio‐temporal semi‐supervised adaptive networks based soft sensor for industrial process
Bibliographic record
Abstract
Abstract Aiming at the question of information loss between layers when mining spatiotemporal features of process data and whether pseudo‐labels are generated for unlabelled data, this paper proposes the dynamically scaling spatio‐temporal semi‐supervised adaptive networks based soft sensor for industrial process (DSST‐SSAN). In order to extract the local temporal correlation features and decrease the information loss between layers, the dynamic scaled spatio‐temporal feature module is constructed, the local prediction models between the current input and the hidden layer features are built in each hidden layer of the long short‐term memory (LSTM) network respectively, the prediction deviations of multiple local models are calculated and the dynamic scaled factors are constructed to update the corresponding hidden layer features. The spatial features are extracted in parallel using graph attention network (GAT), and the spatio‐temporal features are obtained by fusion to establish a soft sensor model. To address the lack of modelling labelling data, a semi‐supervised thresholding mechanism is proposed to filter the pseudo‐labelled data for adaptive data accumulation. The threshold is constructed using the likelihood root mean square of the root mean square error (RMSE) and mean absolute error (MAE) of the labelled data, which can determine whether the unlabelled data need to generate pseudo‐labels and perform modelling data accumulation and thus update the model. The effectiveness of the proposed method is confirmed by simulation experiments on two industrial cases, debutane tower and sulphur recovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".